import pdb

import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torchvision

from ..util import safe_load_state_dict
from .models import register_model

__all__ = ['DRN', 'drn26', 'drn42', 'drn58']


model_urls = {
    'drn26': 'https://tigress-web.princeton.edu/~fy/drn/models/drn26-ddedf421.pth',
    'drn42': 'https://tigress-web.princeton.edu/~fy/drn/models/drn42-9d336e8c.pth',
    'drn58': 'https://tigress-web.princeton.edu/~fy/drn/models/drn58-0a53a92c.pth'
}


def conv3x3(in_planes, out_planes, stride=1, padding=1, dilation=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=padding, bias=False, dilation=dilation)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None,
                 dilation=(1, 1), residual=True):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride,
                             padding=dilation[0], dilation=dilation[0])
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes,
                             padding=dilation[1], dilation=dilation[1])
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
        self.residual = residual

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)
        if self.residual:
            out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None,
                 dilation=(1, 1), residual=True):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=dilation[1], bias=False,
                               dilation=dilation[1])
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class DRN(nn.Module):

    transform = torchvision.transforms.Compose([
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize(
            mean=[0.485, 0.456, 0.406],
            std=[0.229, 0.224, 0.225]),
        ])

    def __init__(self, block, layers, num_cls=1000,
                 channels=(16, 32, 64, 128, 256, 512, 512, 512),
                 out_map=False, out_middle=False, pool_size=28, 
                 weights_init=None, pretrained=True, finetune=False,
                 output_last_ft=False, modelname='drn26'):
        if output_last_ft:
            print('DRN discrim feat not implemented, using scores')

        super(DRN, self).__init__()
        self.inplanes = channels[0]
        self.out_map = out_map
        self.out_dim = channels[-1]
        self.out_middle = out_middle
        self.conv1 = nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(channels[0])
        self.relu = nn.ReLU(inplace=True)

        self.layer1 = self._make_layer(BasicBlock, channels[0], layers[0], stride=1)
        self.layer2 = self._make_layer(BasicBlock, channels[1], layers[1], stride=2)

        self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2)
        self.layer4 = self._make_layer(block, channels[3], layers[3], stride=2)
        self.layer5 = self._make_layer(block, channels[4], layers[4], dilation=2,
                                       new_level=False)
        self.layer6 = None if layers[5] == 0 else \
            self._make_layer(block, channels[5], layers[5], dilation=4,
                             new_level=False)
        self.layer7 = None if layers[6] == 0 else \
            self._make_layer(BasicBlock, channels[6], layers[6], dilation=2,
                             new_level=False, residual=False)
        self.layer8 = None if layers[7] == 0 else \
            self._make_layer(BasicBlock, channels[7], layers[7], dilation=1,
                             new_level=False, residual=False)

        if num_cls > 0:
            self.avgpool = nn.AvgPool2d(pool_size)
            # self.fc = nn.Linear(self.out_dim, num_classes)
            self.fc = nn.Conv2d(self.out_dim, num_cls, kernel_size=1,
                                stride=1, padding=0, bias=True)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

        if pretrained:
            if not weights_init is None:
                state_dict = torch.load(weights_init)
                print('Using state dict from', weights_init)
            else:
                state_dict = model_zoo.load_url(model_urls[modelname])
            
            if finetune:
                del state_dict['fc.weight']
                del state_dict['fc.bias']
                safe_load_state_dict(self, state_dict)
                print('Finetune: remove last layer')
            else:
                self.load_state_dict(state_dict)
                print('Loading full model')
       

    def _make_layer(self, block, planes, blocks, stride=1, dilation=1,
                    new_level=True, residual=True):
        assert dilation == 1 or dilation % 2 == 0
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(
            self.inplanes, planes, stride, downsample,
            dilation=(1, 1) if dilation == 1 else (
                dilation // 2 if new_level else dilation, dilation),
            residual=residual))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, residual=residual,
                                dilation=(dilation, dilation)))

        return nn.Sequential(*layers)

    def forward(self, x):
        _, _, h, w = x.size()
        y = list()

        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.layer1(x)
        y.append(x)
        x = self.layer2(x)
        y.append(x)

        x = self.layer3(x)
        y.append(x)

        x = self.layer4(x)
        y.append(x)

        x = self.layer5(x)
        y.append(x)

        if self.layer6 is not None:
            x = self.layer6(x)
            y.append(x)

        if self.layer7 is not None:
            x = self.layer7(x)
            y.append(x)

        if self.layer8 is not None:
            x = self.layer8(x)
            y.append(x) 

        if self.out_map:
            x = self.fc(x)
            x = nn.functional.upsample(x, (h, w), mode='bilinear', align_corners=True)
        else:
            x = self.avgpool(x)
            x = self.fc(x)
            x = x.view(x.size(0), -1)

        if self.out_middle:
            return x, y
        else:
            return x

@register_model('drn26')
def drn26(pretrained=True, finetune=False, out_map=True, **kwargs):
    model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], modelname='drn26', 
            out_map=out_map, finetune=finetune, **kwargs)
    #if pretrained:
    #    state_dict = model_zoo.load_url(model_urls['drn26'])
    #    if finetune:
    #        del state_dict['fc.weight']
    #        del state_dict['fc.bias']
    #        safe_load_state_dict(model, state_dict)
    #    else:
    #        model.load_state_dict(state_dict)
    return model


@register_model('drn42')
def drn42(pretrained=False, finetune=False, out_map=True, **kwargs):
    model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], modelname='drn42', 
            out_map=out_map, finetune=finetune, **kwargs)
    #if pretrained:
    #    model.load_state_dict(model_zoo.load_url(model_urls['drn42']))
    return model


def drn58(pretrained=False, **kwargs):
    model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn58']))
    return model

